Emotional design method of product presented in multi-dimensional variables based on Kansei Engineering

Guo, Fu; Liu, Wei Lin; Liu, Fan Tao; Wang, Huan; Wang, Tian Bo

doi:10.1080/09544828.2014.944488

Cited by 73 publications

(41 citation statements)

References 32 publications

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“…For example, in this case study, the MARs for car profile were "Modern," "Simple," "Rounded," and "Comfort." The contribution of "Modern, Simple, Rounded, and Comfort" may not simply be equal to the sum of the contributions of "Modern," "Simple," "Rounded," and "Comfort," and there exist interactive effects Although car profile design is used as an example, the proposed approach can be applied to other products with various design variables, such as mobile phones [5], digital cameras [4], and running shoes [45]. The proposed method is somewhat complicated and involves many calculation steps.…”

Section: Discussionmentioning

confidence: 99%

“…Affective engineering, also called Kansei engineering, is a consumer centric technique for product design that aims to translate affective responses into design variables [2]. Many studies have been performed on optimizing product form design by using affective engineering [3][4][5]. In these studies, a questionnaire was commonly used to collect evaluation data, and only the mean scale rating was adopted [6].…”

Section: Introductionmentioning

confidence: 99%

“…The affective responses of humans to products often have various facets. To acquire consumers' preference weights for multiple affective responses (MARs), numerous studies have used questionnaires with single answers [3,4]. However, as fuzzy statistics scholars have indicated that human thinking cannot be measured or described with a single option [21].…”

Section: Introductionmentioning

confidence: 99%

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Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Shieh

Yang

et al. 2018

Mathematical Problems in Engineering

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In today’s competitive market, industrial product form design is moving towards being consumer centric. Affective responses relate to customers’ affective needs and are receiving increasing attention. To design a product form that can appeal to consumers, designers should consider multiple affective responses (MARs). This paper proposes a robust design approach that uses a fuzzy-based hybrid Taguchi method to derive the optimal product form design concerning MARs. First, design analysis is used to identify design variables and MARs. According to the results, a Taguchi experiment is designed in which fuzzy sets are used to measure the MARs; then, signal-to-noise (S/N) ratios are calculated. Subsequently, a fuzzy questionnaire with multiple answers is employed to acquire consumers’ preference weights for MARs, following which Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is adopted to transform the multiple S/N ratios into a multiperformance characteristic index (MPCI). On the basis of the MPCI, the effects of design variables are identified through analysis of variance and the response table and the response graph are obtained. Consequently, the optimal form design is achieved. A car profile design was used as an example to demonstrate the proposed approach. The results indicate that this approach can effectively improve consumers’ affective response qualities and can be used as a robust design approach to optimize product form design.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Shieh

Yang

et al. 2018

Mathematical Problems in Engineering

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“…In the MOPM of MOO module, the PC scores and IEMSs, which are input and output variables, respectively, are continuous data. Wang et al [28] demonstrated that the prediction and fitting ability of neural network model for continuous data is more stable and stronger than the multiple linear regression model used in current authors' previous research [14], and the combination of GABP can further better predict the nonlinear mapping relationship between input and output variables [6,29,30].…”

Section: Genetic Algorithm and Back Propagation (Gabp)mentioning

confidence: 94%

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

et al. 2019

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As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method's high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method's good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form. Appl. Sci. 2019, 9, 2944 2 of 26 apply aggregation on the objectives to transform multiple objectives into a single objective rather than directly using MOEA in the MOO module [4]. Hsiao and Tsai [5] integrated the multiple objectives into a single value by using the linear weighting method, and then adopted a genetic algorithm (GA) to get the optimal product form based on the prediction model constructed by a fuzzy neural network (FNN). Guo et al. [6] also integrated multiple objectives into a single objective by using the linear weighting method, and obtained the optimal design by employing GA on the basis of the prediction model established by using a back propagation neural network (BPNN). This kind of study is simple and easy to implement, but because it limits the search space and excludes the consideration of all possible solutions, it has limited practical value for designers and consumers [7]. The second kind of study is to use MOEA directly in the MOO module. Although the application is more complicated, it has become a mainstream trend because it can better meet the performance requirements of PFDMOEAM. Yang [8] proposed a PFDMOEAM with a non-dominated sorting genetic algorithm II (NSGA-II) as MOEA in the MOO module, and used support vector regression (SVR) to provide multi-objective fitness functions required by NSGA-II. Su et al. [9] also constructed a PFDMOEAM with NSGA-II as MOEA. The difference is that fuzzy neural network (FNN) is used to provide fitness functions for NSGA II. Shieh et al.[10] also adopted NSGA-II as MOEA and combined with quantificat...

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“…A car profile design was employed as a case study to illustrate the proposed approach. The results suggest that the proposed approach is time-and cost-efficient and can effectively extract design knowledge that provides suitable insight into product form design.traditional approach for solving an MOP involves integrating multiple objectives into a single-objective and then using the optimisation algorithm to derive a single optimal solution (Guo et al, 2014;Hsiao et al, 2010); that is, the MOP is converted into a single-objective optimisation problem. This approach is simple and efficient; however, its limitations prevent consideration of all possible solutions, and it cannot provide sufficient knowledge regarding design variables and affective responses.…”

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confidence: 99%

Extracting knowledge for product form design by using multiobjective optimisation and rough sets

Zhu

2020

JAMDSM

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Industrial product form design has become consumer-centred. Affective responses related to consumers' affective needs are considered invaluable for product form design and have attracted increasing attention. When designing product forms, designers should thoroughly understand the design knowledge concerning multiple affective responses and design variables. This paper proposes a systematic approach to extraction of design knowledge by using multiobjective optimisation and rough sets. Design analysis is first employed to determine design variables and multiple affective responses. As per the results, a multiobjective optimisation model is constructed that involves optimising the multiple affective responses. An improved version of the strength Pareto evolutionary algorithm (SPEA2) is adopted to solve the multiobjective optimisation model and generate the Pareto optimal solutions. Based on these Pareto optimal solutions, rough sets are employed to extract design knowledge that is common to these Pareto optimal solutions. A car profile design was employed as a case study to illustrate the proposed approach. The results suggest that the proposed approach is time-and cost-efficient and can effectively extract design knowledge that provides suitable insight into product form design.traditional approach for solving an MOP involves integrating multiple objectives into a single-objective and then using the optimisation algorithm to derive a single optimal solution (Guo et al., 2014;Hsiao et al., 2010); that is, the MOP is converted into a single-objective optimisation problem. This approach is simple and efficient; however, its limitations prevent consideration of all possible solutions, and it cannot provide sufficient knowledge regarding design variables and affective responses. Thus, the traditional optimisation approach constrains their usefulness to designers (Shieh et al., 2017). By contrast, a multiobjective optimisation approach does not require the integration of multiple objectives and can provide numerous Pareto optimal solutions. Based on these Pareto optimal solutions, designers can extract knowledge for product form design (Deb and Srinivasan, 2006).Rough sets are an effective and systematic method for knowledge discovery. They can be used to analyse all types of data, including linear and nonlinear data (Nagamachi et al., 2006). The main difference between rough sets and other approaches coping with uncertain problems is that rough sets do not require any prior information beyond the problem itself . Because of these advantages, rough sets have been used in many studies for extracting design knowledge . Zhai et al. (2009b) employed rough sets to analyse the imprecise affective responses of consumers and acquire design knowledge for improving consumer satisfaction with product design. Shi et al. (2012) used rough sets to reduce the design variables of product form design and then employed association rule mining to extract design principles. In this study, we used rough sets to extract design knowledge from...

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Emotional design method of product presented in multi-dimensional variables based on Kansei Engineering

Cited by 73 publications

References 32 publications

Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

Extracting knowledge for product form design by using multiobjective optimisation and rough sets

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